• 
  KAIST 산업및시스템공학과 문일철 교수

  KAIST 산업및시스템공학과 교수
  KAIST 김재철AI대학원 겸임교수
  KAIST 항공우주공학과 겸임교수
  KAIST 안보융합연구원 겸임교수
  한국인공지능학회 교육이사

1. CHAPTER 1. System Dynamics
   1. 1-1. Tragedy of the Commons
   1. 1-2. Casual Loop Diagram
   1. 1-3. System Archetypes 1
   1. 1-4. System Archetypes 2
   1. 1-5. From Causal loop Diagram to System Dynamics
   1. 1-6. System Dynamics Pattern
   1. 1-7. System Dynamics for Grass Growth
   1. PDF
   1. Chapter 1. Quiz
2. CHAPTER 2. Discrete Event Simulation – Process Oriented View
   1. 1. Simulation time flow and Finite State Machine
   1. 2. Petri-Net and its Notations
   1. 3. Simulation and patterns of Petri-Nets
   1. 4. Properties and Example of Petri-Net
   1. PDF
   1. Chapter 2. Quiz
3. CHAPTER 3. Discrete Event Simulation – Object Oriented View
   1. 1. Eight Queen problem
   1. 2. Automaton
   1. 3. Rule 30
   1. 4. Formalism of Cellular Automata
   1. 5, Examples of Cellular Automata
   1. PDF
   1. Chapter 3. Quiz
4. CHAPTER 4. Discrete Event Simulation – DEVS Formalism
   1. 1. Detour
   1. 2. DEVS Formalism
   1. 3. DEVS Atomic Model (1)
   1. 4. DEVS Atomic Model (2)
   1. 5. DEVS Coupled Model (1)
   1. 6. DEVS Coupled Model (2)
   1. PDF
   1. Chapter 4. Quiz
5. CHAPTER 5. Discrete Event Simulation – Agent-Based Model
   1. 1. Supply and demand model
   1. 2. Discussion on Readings
   1. 3. Complexity and Emergence (1)
   1. 4. Complexity and Emergence (2)
   1. 5. Model Registration in Repast
   1. 6. Basic Structure of Agents
   1. 7. The Beginning of Complex Models
   1. 8. Simple Reflex Agent Model
   1. PDF
   1. Chapter 5. Quiz
6. ★강의 수강 후 의견을 부탁드리겠습니다.★
   1. 교수님 강의에 대한 별점을 매겨주세요. 여러분의 의견이 많은 도움이 됩니다:D

1. 오프라인 수업_코드 실습 영상
   1. Lecture 1
   1. System Dynamics_1. Causal Loop Diagram and System Dynamics
   1. System Dynamics_2. System Dynamics Pattern
   1. System Dynamics_3. Exercise-AnyLogic
   1. Petri Net
   1. Cellular Automata
   1. Agent Based Model
   1. Simulation Engine
   1. Advanced Process-Oriented Model
   1. Statistical Analyses on Virtual Experiments
   1. Statistical Analyses on Virtual Experiments_SIGMA
   1. Statistical Analyses on Virtual Experiments_ARENA
   1. Statistical Analyses on Virtual Experiments_ANYLOGIC

본 강좌는 Python3 를 기반으로 진행되는 강좌 입니다.
기초적인 내용부터 시작하기 때문에, 누구나 수강할 수 있는 강좌입니다.
-----------------------------------------------------------------------------------------
* 강좌 수료 기준 충족 시 수료증을 제공합니다:)